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Disturbance regimes can strongly influence geographic patterns of biodiversity. The types of disturbances and their frequencies can have varying impacts on different dimensions of biodiversity and taxonomic groups, and their influence can also vary with spatial scale. Yet disturbance layers are lacking at sufficiently high spatial resolution and extent to uncover these relationships with biodiversity. We detected disturbances for the conterminous United States from Landsat time series using the established LandTrendr temporal segmentation with a novel secondary classification that incorporates spatial context. We then included these disturbance layers, aggregated to metrics at different temporal and spatial scales, into model of species richness at National Ecological Observatory Network sites.

Predictions from species distribution models (SDMs) are commonly used in support of environmental decision-making to explore potential impacts of climate change on biodiversity. However, because future climates are likely to differ from current climates, there has been ongoing interest in understanding the ability of SDMs to predict species responses under novel conditions (i.e., model transferability). Here, we explore the spatial and environmental limits to extrapolation in SDMs using forest inventory data from 11 model algorithms for 108 tree species across the western United States. Algorithms performed well in predicting occurrence for plots that occurred in the same geographic region in which they were fitted. However, a substantial portion of models performed worse than random when predicting for geographic regions in which algorithms were not fitted. Our results suggest that for transfers in geographic space, no specific algorithm was better than another as there were no significant differences in predictive performance across algorithms. There were significant differences in predictive performance for algorithms transferred in environmental space with GAM performing best. However, the predictive performance of GAM declined steeply with increasing extrapolation in environmental space relative to other algorithms. The results of this study suggest that SDMs may be limited in theirmore »ability to predict species ranges beyond the environmental data used for model fitting. When predicting climate-driven range shifts, extrapolation may also not reflect important biotic and abiotic drivers of species ranges, and thus further misrepresent the realized shift in range. Future studies investigating transferability of process based SDMs or relationships between geodiversity and biodiversity may hold promise.« less

Global declines in biodiversity have the potential to affect ecosystem function, and vice versa, in both terrestrial and aquatic ecological realms. While many studies have considered biodiversity-ecosystem function (BEF) relationships at local scales within single realms, there is a critical need for more studies examining BEF linkages among ecological realms, across scales, and across trophic levels. We present a framework linking abiotic attributes, productivity, and biodiversity across terrestrial and inland aquatic realms. We review examples of the major ways that BEF linkages form across realms–cross-system subsidies, ecosystem engineering, and hydrology. We then formulate testable hypotheses about the relative strength of these connections across spatial scales, realms, and trophic levels. While some studies have addressed these hypotheses individually, to holistically understand and predict the impact of biodiversity loss on ecosystem function, researchers need to move beyond local and simplified systems and explicitly investigate cross-realm and trophic interactions and large-scale patterns and processes. Recent advances in computational power, data synthesis, and geographic information science can facilitate studies spanning multiple ecological realms that will lead to a more comprehensive understanding of BEF connections.

1. The geodiv r package calculates gradient surface metrics from imagery and other gridded datasets to provide continuous measures of landscape heterogeneity for landscape pattern analysis. 2. geodiv is the first open-source, command line toolbox for calculating many gradient surface metrics and easily integrates parallel computing for applications with large images or rasters (e.g. remotely sensed data). All functions may be applied either globally to derive a single metric for an entire image or locally to create a texture image over moving windows of a user-defined extent. 3. We present a comprehensive description of the functions available through geodiv. A supplemental vignette provides an example application of geodiv to the fields of landscape ecology and biogeography. 4. geodiv allows users to easily retrieve estimates of spatial heterogeneity for a variety of purposes, enhancing our understanding of how environmental structure influences ecosystem processes. The package works with any continuous imagery and may be widely applied in many fields where estimates of surface complexity are useful.

As the effects of anthropogenic climate change become more severe, several approaches for deliberate climate intervention to reduce or stabilize Earth’s surface temperature have been proposed. Solar radiation modification (SRM) is one potential approach to partially counteract anthropogenic warming by reflecting a small proportion of the incoming solar radiation to increase Earth’s albedo. While climate science research has focused on the predicted climate effects of SRM, almost no studies have investigated the impacts that SRM would have on ecological systems. The impacts and risks posed by SRM would vary by implementation scenario, anthropogenic climate effects, geographic region, and by ecosystem, community, population, and organism. Complex interactions among Earth’s climate system and living systems would further affect SRM impacts and risks. We focus here on stratospheric aerosol intervention (SAI), a well-studied and relatively feasible SRM scheme that is likely to have a large impact on Earth’s surface temperature. We outline current gaps in knowledge about both helpful and harmful predicted effects of SAI on ecological systems. Desired ecological outcomes might also inform development of future SAI implementation scenarios. In addition to filling these knowledge gaps, increased collaboration between ecologists and climate scientists would identify a common set of SAI research goalsmore »and improve the communication about potential SAI impacts and risks with the public. Without this collaboration, forecasts of SAI impacts will overlook potential effects on biodiversity and ecosystem services for humanity.« less

Global loss of biodiversity and its associated ecosystem services is occurring at an alarming rate and is predicted to accelerate in the future. Metacommunity theory provides a framework to investigate multi-scale processes that drive change in biodiversity across space and time. Short-term ecological studies across space have progressed our understanding of biodiversity through a metacommunity lens, however, such snapshots in time have been limited in their ability to explain which processes, at which scales, generate observed spatial patterns. Temporal dynamics of metacommunities have been understudied, and large gaps in theory and empirical data have hindered progress in our understanding of underlying metacommunity processes that give rise to biodiversity patterns. Fortunately, we are at an important point in the history of ecology, where long-term studies with cross-scale spatial replication provide a means to gain a deeper understanding of the multiscale processes driving biodiversity patterns in time and space to inform metacommunity theory. The maturation of coordinated research and observation networks, such as the United States Long Term Ecological Research (LTER) program, provides an opportunity to advance explanation and prediction of biodiversity change with observational and experimental data at spatial and temporal scales greater than any single research group could accomplish. Synthesismore »of LTER network community datasets illustrates that long-term studies with spatial replication present an under-utilized resource for advancing spatio-temporal metacommunity research. We identify challenges towards synthesizing these data and present recommendations for addressing these challenges. We conclude with insights about how future monitoring efforts by coordinated research and observation networks could further the development of metacommunity theory and its applications aimed at improving conservation efforts.« less